Optimization of the Nested Monte-Carlo Algorithm on the Traveling Salesman Problem with Time Windows

The traveling salesman problem with time windows is known to be a really difficult benchmark for optimization algorithms. In this paper, we are interested in the minimization of the travel cost. To solve this problem, we propose to use the nested Monte-Carlo algorithm combined with a Self-Adaptation Evolution Strategy. We compare the efficiency of several fitness functions. We show that with our technique we can reach the state of the art solutions for a lot of problems in a short period of time.

• Type: Journal
• Year: 2011
• Where: EVOW
• Authors: Arpad Rimmel, Fabien Teytaud, Tristan Cazenave
• Keywords: Artificial Intelligence, EVOW 2011, Monte Carlo Algorithm, Optimization Algorithms, Traveling Salesman Problem